Introduction

1 2 3 4 5 6

Key Questions

  • How does HIV persist and migrate across CNS and peripheral tissues despite systemic viral suppression?

Overview

This presentation summarizes:

  • Viral phylogenetics & BEAST modeling
  • CNS reservoir compartmentalization
  • Migration dynamics across tissues & BBB



Cohort Workflow

Last Gift Cohort

Body Donation

Autopsy

Tissue Collection

HIV Sequencing

Phylogenetics and Modeling

OBJECTIVES

Blood Brain Interface

Image attribution: BioRender.
(click anywhere to close)

Objectives

Aim 1

  • Primary objectives:
    • model viral migration events across the Blood Brain Barrier (CNS ↔︎ periphery).
    • identify CSF markers and reservoir characteristics associated with dynamics
  • Secondary objectives:
    • model viral migration events within the CNS.
    • identify CSF markers and reservoir characteristics associated with CNS dynamics

Aim 2

  • Identify CNS-specific clusters (reservoirs)
  • Characterize:
    • Composition
    • Timing
    • Renewal from periphery
    • Reseeding capacity into peripheral reservoirs

APPROACH

Aim 1

Data Processing

  • SGA HIV env clones across tissues
  • CSF biomarkers & clinical metadata
  • BEAST diffusion + phylogenetic modeling
  • Negative Binomial & Poisson models

Workflow

Aim 2

Data Processing

  • HIV env clones across participants & tissues
  • Cleanup & alignment
  • Phylogenetic reconstruction
  • Identify CNS-specific supported clusters
  • Bayesian skyline / skygrid diffusion models
  • Timing (tMRCA) of clusters
  • Migration flows between CNS clusters & other tissues
  • Modeling factors associated with migration

Workflow

Sampling

Note

  • Our analyses included 20 participants with sequences from both the CNS and Periphery.
  • A total of 1875 SGA clones were included from 49 distincts tissues.
  • Luminex CSF data available for 34 markers.

AIM 1: Viral Dynamics

Maximum Clade Credibility Tree

Time-Scaled Maximum Clade Credibility Tree

HIV Migration Networks

HIV Flow across the BBB through time

AIM 1: Reservoirs Dynamic and CSF Markers

Summary

Objectives

  • Primary objectives:
    • model viral migration events across the Blood Brain Barrier (CNS ↔︎ periphery).
    • identify CSF markers and reservoir characteristics associated with dynamics
  • Secondary objectives:
    • model viral migration events within the CNS.
    • identify CSF markers and reservoir characteristics associated with CNS dynamics

Approach

  • We compared a patient-level aggregated negative-binomial model (AGG-NB) with a state-level hierarchical negative-binomial model (STATE-NB) that accounted for variation across individual BEAST states.

Data Preparation

  • Aggregated (pid-level counts):We first fit an aggregated-count model (mod_agg_nb), where counts were summed across BEAST states for each participant.”
  • Longitudinal (state-level / transition-level): We then fit a state-level model (mod_long_nb), leveraging the full longitudinal BEAST reconstruction.

Exposure (log)

  • Aggregated (pid-level counts): product of n_states* n_seq_cns*n_seq_periphery.
  • Longitudinal (state-level / transition-level): product of n_seq_source*n_seq_destination

Data Input

Exposure

Conceptual motivation

In a transition model, exposure represents the “opportunity” for observing a migration event between compartments. We considered the following definition:

  • Aggregated (pid-level counts): product of n_states* n_seq_cns*n_seq_periphery.
  • Longitudinal (state-level / transition-level): product of n_seq_source*n_seq_destination

Equation

\[ n_{\text{states}, i} : \text{number of Markov states in run } i \times \text{number of sequences from CNS} \times \text{number of sequences from Periphery} \]

Model Code

data %>%
  mutate(
    pairs_cns    = ntissues_cns * (ntissues_cns - 1) / 2,
    pairs_periph = ntissues_periph * (ntissues_periph - 1) / 2,
    
    exposure = case_when(
      migration_type == "cross_BBB" & direction == "CNS to Periph" ~ 
        n_states * nseq_cns * nseq_periph,
      
      migration_type == "cross_BBB" & direction == "Periphery to CNS" ~ 
        n_states * nseq_periph * nseq_cns,
      
      migration_type == "within_CNS" ~ 
        n_states * pairs_cns * (nseq_cns^2),
      
      migration_type == "within_peripheral" ~ 
        n_states * pairs_periph * (nseq_periph^2)
    )
  )

CSF Markers

Data Preparation for Migration Models

Summaries

  • Count migration/transition events from and toward the CNS and within CNS
  • Compute number of discrete viral states per run
  • Count SGA clones sampled in CNS and in Periphery

Exposure Definition

  • Aggregated: # states × # CNS sequences × # Periphery sequences
  • long format: # sequences from × # sequences to

Filtering

  • Exclude viremic individuals
  • Exclude individuals without CSF data

Data Transformations

  • Log-transform exposure
  • Log1p-transform CSF marker values and reservoir measures

Workflow

Model Specification: Negative Binomial GLMM

Notes

  • Outcome: count of CNS-related migration/transition events
  • Offset: log(exposure)
  • Predictor: log1p-transformed CSF marker
  • Random effect for participants

Equation

\[ \log(\mu_i) = \beta_0 + \beta_1 \cdot \text{Marker}_i + b_{\text{pid}(i)} + \log(\text{Exposure}_i) \]

fit_models <- function(marker, data_in) {
  formula <- as.formula(
    paste0(
      "n_events ~ ", marker,
      " + age + sex + last_cd4_t_cell_count + duration_infection_years +
        (1 | pid) + offset(log_exposure)"
    )
  )

  glmmTMB(
    formula,
    data = data_in,
    family = nbinom2(),
    control = glmmTMBControl(
      optimizer = optim,
      optArgs = list(method = "BFGS", maxit = 5000)
    )
  )
}
fit_models <- function(marker, data_in) {
  formula <- as.formula(
    paste0(
      "n_events ~ ", marker,
      " + age + sex + last_cd4_t_cell_count + duration_infection_years +
      dna_level_from + dna_level_to +
        (1 | pid) + offset(log_exposure)"
    )
  )

  glmmTMB(
    formula,
    data = data_in,
    family = nbinom2(),
    control = glmmTMBControl(
      optimizer = optim,
      optArgs = list(method = "BFGS", maxit = 5000)
    )
  )
}

Model Results: Aggregated (pid-level counts)

Associations between CSF biomarkers and counts of migration events from the CNS to the periphery
Negative Binomial Models
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 13 Ok 1.042 0.7996–1.36 0.759
GM-CSF 8 Ok 0.890 0.7506–1.06 0.180
GRO-alpha (CXCL1) 13 Ok 0.988 0.9805–1.00 0.001 **
IL-1α 1 Sparse Data 0.236 0.0031–17.88 0.513
IL-1β 5 Ok 0.839 0.6518–1.08 0.173
IL-1RA 12 Ok 1.000 0.9995–1.00 0.000 ***
IL-2 2 Sparse Data 0.419 0.2598–0.67 0.000 ***
IL-5 5 Ok 0.985 0.8074–1.20 0.877
IL-6 12 Ok 0.998 0.9971–1.00 0.000 ***
IL-7 13 Ok 0.245 0.0441–1.37 0.109
IL-8 (CXCL8) 13 Ok 0.998 0.9954–1.00 0.089 .
IL-9 1 Sparse Data 1.165 0.6152–2.20 0.640
IL-10 8 Ok 0.511 0.2750–0.95 0.033 *
IL-15 5 Ok 0.491 0.1756–1.38 0.176
IL-17A/CTLA-8 1 Sparse Data 0.327 0.0442–2.42 0.274
IL-18 13 Ok 1.005 0.9827–1.03 0.686
IL-31 1 Sparse Data 0.926 0.6711–1.28 0.641
IP-10 (CXCL10) 13 Ok 1.000 0.9969–1.00 0.926
MCP-1 (CCL2) 13 Ok 1.000 0.9988–1.00 0.953
MIP-1α (CCL3) 13 Ok 0.871 0.7684–0.99 0.031 *
MIP-1β (CCL4) 13 Ok 0.965 0.9424–0.99 0.002 **
RANTES (CCL5) 13 Ok 1.009 0.9194–1.11 0.849
SDF-1α 13 Ok 1.000 0.9994–1.00 0.751
TNF-α 2 Sparse Data 0.326 0.2118–0.50 0.000 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events from the periphery to the CNS
Negative Binomial Models
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 16 Ok 0.970 0.864–1.09 0.607
GM-CSF 11 Ok 0.878 0.780–0.99 0.031 *
GRO-alpha (CXCL1) 16 Ok 0.986 0.981–0.99 0.000 ***
IL-1α 1 Sparse Data 0.230 0.014–3.92 0.310
IL-1β 7 Ok 0.873 0.753–1.01 0.072 .
IL-1RA 15 Ok 1.000 1.000–1.00 0.001 ***
IL-2 2 Sparse Data 0.380 0.304–0.48 0.000 ***
IL-5 7 Ok 0.915 0.713–1.17 0.486
IL-6 15 Ok 0.998 0.997–1.00 0.000 ***
IL-7 16 Ok 0.866 0.232–3.23 0.831
IL-8 (CXCL8) 16 Ok 0.999 0.997–1.00 0.162
IL-9 1 Sparse Data 1.205 0.712–2.04 0.487
IL-10 10 Ok 0.916 0.765–1.10 0.339
IL-15 7 Ok 0.378 0.183–0.78 0.009 **
IL-17A/CTLA-8 2 Sparse Data 0.584 0.225–1.51 0.268
IL-18 16 Ok 1.011 0.995–1.03 0.179
IL-31 1 Sparse Data 0.908 0.718–1.15 0.422
IP-10 (CXCL10) 16 Ok 1.000 0.999–1.00 0.398
MCP-1 (CCL2) 16 Ok 1.000 0.999–1.00 0.862
MIP-1α (CCL3) 16 Ok 0.904 0.839–0.97 0.008 **
MIP-1β (CCL4) 16 Ok 0.983 0.968–1.00 0.026 *
RANTES (CCL5) 16 Ok 0.990 0.933–1.05 0.748
SDF-1α 16 Ok 1.000 0.999–1.00 0.494
TNF-α 3 Sparse Data 0.362 0.234–0.56 0.000 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events across the BBB
Negative Binomial Models
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 16 Ok 1.014 0.8655–1.19 0.866
GM-CSF 11 Ok 0.892 0.7872–1.01 0.071 .
GRO-alpha (CXCL1) 16 Ok 0.988 0.9817–1.00 0.001 ***
IL-1α 1 Sparse Data 0.330 0.0082–13.26 0.557
IL-1β 7 Ok 0.875 0.7033–1.09 0.234
IL-1RA 15 Ok 1.000 0.9995–1.00 0.001 ***
IL-2 2 Sparse Data 0.393 0.2965–0.52 0.000 ***
IL-5 7 Ok 0.926 0.7871–1.09 0.354
IL-6 15 Ok 0.998 0.9972–1.00 0.001 ***
IL-7 16 Ok 1.010 0.2351–4.34 0.989
IL-8 (CXCL8) 16 Ok 0.999 0.9970–1.00 0.174
IL-9 1 Sparse Data 1.106 0.6638–1.84 0.699
IL-10 10 Ok 0.952 0.7445–1.22 0.698
IL-15 7 Ok 0.413 0.1850–0.92 0.031 *
IL-17A/CTLA-8 2 Sparse Data 0.825 0.2375–2.86 0.762
IL-18 16 Ok 1.010 0.9922–1.03 0.284
IL-31 1 Sparse Data 0.968 0.7310–1.28 0.820
IP-10 (CXCL10) 16 Ok 1.000 0.9986–1.00 0.834
MCP-1 (CCL2) 16 Ok 1.000 0.9989–1.00 0.854
MIP-1α (CCL3) 16 Ok 0.876 0.7832–0.98 0.020 *
MIP-1β (CCL4) 16 Ok 0.982 0.9598–1.01 0.126
RANTES (CCL5) 16 Ok 1.007 0.9299–1.09 0.872
SDF-1α 16 Ok 1.000 0.9990–1.00 0.848
TNF-α 3 Sparse Data 0.357 0.2330–0.55 0.000 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events within CNS
Negative Binomial Models
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 15 Ok 1.046 8.5e-01–1.294 0.680
GM-CSF 10 Ok 0.897 7.2e-01–1.114 0.326
GRO-alpha (CXCL1) 15 Ok 0.987 9.8e-01–0.998 0.019 *
IL-1α 1 Sparse Data 0.001 1.5e-05–0.039 0.000 ***
IL-1β 6 Ok 0.878 6.8e-01–1.128 0.309
IL-1RA 14 Ok 0.999 1.0e+00–1.000 0.000 ***
IL-2 2 Sparse Data 0.405 2.4e-01–0.683 0.001 ***
IL-5 7 Ok 1.127 7.6e-01–1.678 0.558
IL-6 14 Ok 0.998 1.0e+00–0.999 0.004 **
IL-7 15 Ok 0.602 1.3e-01–2.870 0.524
IL-8 (CXCL8) 15 Ok 0.999 1.0e+00–1.002 0.586
IL-9 1 Sparse Data 0.491 2.0e-01–1.225 0.127
IL-10 9 Ok 1.019 7.2e-01–1.434 0.915
IL-15 7 Ok 0.171 4.0e-02–0.735 0.018 *
IL-17A/CTLA-8 2 Sparse Data 1.849 2.8e-01–12.424 0.527
IL-18 15 Ok 1.018 9.9e-01–1.047 0.212
IL-31 1 Sparse Data 0.890 6.4e-01–1.242 0.493
IP-10 (CXCL10) 15 Ok 1.000 1.0e+00–1.001 0.875
MCP-1 (CCL2) 15 Ok 0.999 1.0e+00–1.001 0.281
MIP-1α (CCL3) 15 Ok 0.915 8.0e-01–1.048 0.200
MIP-1β (CCL4) 15 Ok 0.972 9.4e-01–1.010 0.141
RANTES (CCL5) 15 Ok 0.920 8.2e-01–1.035 0.165
SDF-1α 15 Ok 1.000 1.0e+00–1.001 0.537
TNF-α 3 Sparse Data 0.462 2.0e-01–1.042 0.063 .
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.

Model Results: Longitudinal / state-level data

Associations between CSF biomarkers and counts of migration events from the CNS to the periphery
Negative Binomial Models (per transition)
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 13 Ok 1.049 0.923–1.19 0.463
GM-CSF 8 Ok 0.925 0.855–1.00 0.054 .
GRO-alpha (CXCL1) 13 Ok 0.995 0.991–1.00 0.033 *
IL-1α 1 Sparse Data 0.134 0.018–1.02 0.052 .
IL-1β 5 Ok 0.973 0.846–1.12 0.697
IL-1RA 12 Ok 1.000 1.000–1.00 0.000 ***
IL-2 2 Sparse Data 0.695 0.532–0.91 0.008 **
IL-5 5 Ok 0.973 0.885–1.07 0.574
IL-6 12 Ok 0.999 0.999–1.00 0.018 *
IL-7 13 Ok 1.139 0.420–3.09 0.798
IL-8 (CXCL8) 13 Ok 0.998 0.997–1.00 0.000 ***
IL-9 1 Sparse Data 0.853 0.591–1.23 0.394
IL-10 8 Ok 0.608 0.455–0.81 0.001 ***
IL-15 5 Ok 0.827 0.488–1.40 0.480
IL-17A/CTLA-8 1 Sparse Data 0.543 0.198–1.49 0.236
IL-18 13 Ok 1.003 0.991–1.02 0.597
IL-31 1 Sparse Data 1.023 0.863–1.21 0.795
IP-10 (CXCL10) 13 Ok 1.000 0.998–1.00 0.877
MCP-1 (CCL2) 13 Ok 0.999 0.999–1.00 0.000 ***
MIP-1α (CCL3) 13 Ok 0.960 0.889–1.04 0.294
MIP-1β (CCL4) 13 Ok 0.983 0.968–1.00 0.020 *
RANTES (CCL5) 13 Ok 1.004 0.959–1.05 0.874
SDF-1α 13 Ok 1.000 0.999–1.00 0.895
TNF-α 2 Sparse Data 0.598 0.448–0.80 0.000 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events from the periphery to the CNS
Negative Binomial Models (per transition)
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 15 Ok 1.038 0.932–1.16 0.501
GM-CSF 10 Ok 0.898 0.832–0.97 0.005 **
GRO-alpha (CXCL1) 15 Ok 0.993 0.989–1.00 0.005 **
IL-1α 1 Sparse Data 1.373 0.098–19.24 0.814
IL-1β 7 Ok 0.889 0.765–1.03 0.127
IL-1RA 14 Ok 1.000 1.000–1.00 0.015 *
IL-2 2 Sparse Data 0.550 0.437–0.69 0.000 ***
IL-5 6 Ok 0.946 0.845–1.06 0.337
IL-6 14 Ok 0.999 0.998–1.00 0.013 *
IL-7 15 Ok 0.684 0.217–2.16 0.518
IL-8 (CXCL8) 15 Ok 0.999 0.998–1.00 0.313
IL-9 1 Sparse Data 1.297 0.922–1.82 0.135
IL-10 10 Ok 0.848 0.729–0.99 0.032 *
IL-15 6 Ok 0.543 0.325–0.91 0.020 *
IL-17A/CTLA-8 2 Sparse Data 0.701 0.295–1.66 0.420
IL-18 15 Ok 0.999 0.986–1.01 0.818
IL-31 1 Sparse Data 0.955 0.787–1.16 0.643
IP-10 (CXCL10) 15 Ok 1.000 0.999–1.00 0.933
MCP-1 (CCL2) 15 Ok 1.000 0.999–1.00 0.851
MIP-1α (CCL3) 15 Ok 0.928 0.852–1.01 0.082 .
MIP-1β (CCL4) 15 Ok 0.991 0.974–1.01 0.277
RANTES (CCL5) 15 Ok 1.017 0.965–1.07 0.522
SDF-1α 15 Ok 1.000 1.000–1.00 0.862
TNF-α 3 Sparse Data 0.488 0.357–0.67 0.000 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events across the BBB
Negative Binomial Models (per transition)
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 15 Ok 1.038 0.94–1.15 0.460
GM-CSF 10 Ok 0.909 0.85–0.98 0.008 **
GRO-alpha (CXCL1) 15 Ok 0.994 0.99–1.00 0.006 **
IL-1α 1 Sparse Data 0.881 0.08–9.65 0.917
IL-1β 7 Ok 0.901 0.79–1.03 0.137
IL-1RA 14 Ok 1.000 1.00–1.00 0.005 **
IL-2 2 Sparse Data 0.586 0.47–0.73 0.000 ***
IL-5 6 Ok 0.955 0.86–1.06 0.388
IL-6 14 Ok 0.999 1.00–1.00 0.012 *
IL-7 15 Ok 0.754 0.26–2.15 0.598
IL-8 (CXCL8) 15 Ok 0.999 1.00–1.00 0.231
IL-9 1 Sparse Data 1.215 0.88–1.67 0.230
IL-10 10 Ok 0.860 0.75–0.99 0.033 *
IL-15 6 Ok 0.603 0.37–0.98 0.042 *
IL-17A/CTLA-8 2 Sparse Data 0.706 0.32–1.56 0.388
IL-18 15 Ok 0.999 0.99–1.01 0.828
IL-31 1 Sparse Data 0.960 0.81–1.14 0.649
IP-10 (CXCL10) 15 Ok 1.000 1.00–1.00 0.902
MCP-1 (CCL2) 15 Ok 1.000 1.00–1.00 0.649
MIP-1α (CCL3) 15 Ok 0.934 0.86–1.01 0.081 .
MIP-1β (CCL4) 15 Ok 0.990 0.98–1.01 0.211
RANTES (CCL5) 15 Ok 1.016 0.97–1.07 0.511
SDF-1α 15 Ok 1.000 1.00–1.00 0.921
TNF-α 3 Sparse Data 0.525 0.40–0.70 0.000 ***
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.
Associations between CSF biomarkers and counts of migration events within CNS
Negative Binomial Models (per transition)
Marker Samples Detectable1 Effect Status exp(Beta) 95% CI p-value Significance Direction
Eotaxin (CCL11) 15 Ok 1.304 0.91–1.88 0.154
GM-CSF 10 Ok 0.711 0.35–1.43 0.338
GRO-alpha (CXCL1) 15 Ok 0.519 0.28–0.96 0.038 *
IL-1α 1 Sparse Data 0.684 0.52–0.90 0.006 **
IL-1β 6 Ok 0.966 0.53–1.76 0.910
IL-1RA 14 Ok 0.476 0.33–0.69 0.000 ***
IL-2 2 Sparse Data 0.576 0.37–0.90 0.015 *
IL-5 7 Ok 0.835 0.40–1.75 0.633
IL-6 14 Ok 0.510 0.28–0.92 0.024 *
IL-7 15 Ok 1.083 0.67–1.75 0.746
IL-8 (CXCL8) 15 Ok 0.796 0.54–1.17 0.247
IL-9 1 Sparse Data 0.653 0.48–0.89 0.007 **
IL-10 9 Ok 1.061 0.68–1.66 0.797
IL-15 7 Ok 0.666 0.44–1.02 0.060 .
IL-17A/CTLA-8 2 Sparse Data 1.232 0.85–1.78 0.266
IL-18 15 Ok 1.492 0.83–2.68 0.181
IL-31 1 Sparse Data 1.146 0.64–2.04 0.644
IP-10 (CXCL10) 15 Ok 1.195 0.86–1.66 0.292
MCP-1 (CCL2) 15 Ok 0.495 0.30–0.82 0.006 **
MIP-1α (CCL3) 15 Ok 0.834 0.46–1.51 0.551
MIP-1β (CCL4) 15 Ok 0.881 0.60–1.30 0.524
RANTES (CCL5) 15 Ok 1.015 0.68–1.51 0.941
SDF-1α 15 Ok 1.079 0.66–1.75 0.758
TNF-α 3 Sparse Data 0.555 0.32–0.95 0.032 *
1 Number of samples with ≥1 measure(s) above the LOD across the 3 replicates.

AIM 1: Reservoirs Dynamic and Reservoirs Characteristics

Model Results: Longitudinal / state-level data

Objectives

  • Primary objectives:
    • model viral migration events across the Blood Brain Barrier (CNS ↔︎ periphery).
    • identify reservoir characteristics associated with dynamics
Associations between Reservoirs Measures and counts of migration events from the CNS to the periphery
Negative Binomial Models (per transition)
Reservoir Measures Effect Status exp(Beta) 95% CI p-value Significance Direction
HIV DNA (source) Ok 0.885 7.1e-01–1.1e+00 0.277
HIV DNA (destination) Ok 1.017 8.6e-01–1.2e+00 0.847
Viral Diversity (source) Ok >1e6 5.4e+04–+Inf 0.006 **
Viral Diversity (destination) Ok >1e6 7.0e-03–+Inf 0.143
Viral Divergence Wide CI 4.141 <1e-6–+Inf 0.904
Associations between Reservoirs Measures and counts of migration events from the periphery to the CNS
Negative Binomial Models (per transition)
Reservoir Measures Effect Status exp(Beta) 95% CI p-value Significance Direction
HIV DNA (source) Ok 1.085 9.8e-01–1.2e+00 0.114
HIV DNA (destination) Ok 1.052 9.6e-01–1.2e+00 0.287
Viral Diversity (source) Ok 82.170 1.8e-04–+Inf 0.507
Viral Diversity (destination) Ok 4.366 3.7e-06–+Inf 0.836
Viral Divergence Wide CI 0.159 <1e-6–+Inf 0.842
Associations between Reservoirs Measures and counts of migration events across the BBB
Negative Binomial Models (per transition)
Reservoir Measures Effect Status exp(Beta) 95% CI p-value Significance Direction
HIV DNA (source) Ok 1.079 1.0e+00–1.2e+00 0.064 .
HIV DNA (destination) Ok 0.995 9.3e-01–1.1e+00 0.885
Viral Diversity (source) Ok 950493.238 1.0e+01–+Inf 0.019 *
Viral Diversity (destination) Ok 19.716 2.3e-04–+Inf 0.607
Viral Divergence Wide CI 0.254 <1e-6–+Inf 0.863
Associations between Reservoirs Measures and counts of migration events within CNS
Negative Binomial Models (per transition)
Reservoir Measures Effect Status exp(Beta) 95% CI p-value Significance Direction
HIV DNA (source) Ok 0.739 6.3e-01–8.6e-01 0.000 ***
HIV DNA (destination) Ok 1.059 9.4e-01–1.2e+00 0.365
Viral Diversity (source) Wide CI 296.502 <1e-6–+Inf 0.571
Viral Diversity (destination) Ok 14999.046 9.3e-04–+Inf 0.256
Viral Divergence Wide CI 5.242 <1e-6–+Inf 0.868

AIM 2: CNS Compartmentalization Analysis

Approach

Participants

  • All Last Gift individuals
  • Restricted to virally suppressed donors?
  • Only samples with SGA clones available in both CNS and periphery

1. Data Preparation

  • Multiple sequence alignment (MAFFT)
  • Build maximum-likelihood phylogeny (IQ-TREE)
    • GTR+Γ model and 1,000 ultrafast bootstraps

2. Identify CNS-specific Phylogenetic Clusters

  • Define CNS taxa per participant with custom R functions:
find_cns_clusters(tree, cns_taxa, min_size = 5)
filter_nested_clusters(cluster_list)
  • Scan all internal nodes. A cluster is retained if:
    • ≥ 5 tips - originate from CNS only
  • Nested CNS clades are automatically removed

3. Bayesian Temporal Reconstruction (BEAST)

  • Build time-scaled phylogenies for each participant
  • Model: Bayesian Skyline coalescent (MCMC chains ESS > 200)

4. Outputs

  • Time-scaled phylogeny annotated with CNS clusters
  • Posterior density of tMRCA for each CNS-specific cluster

Workflow

Results Summary

Note

  • Our analyses included 18 participants with sequences from both the CNS and Periphery.
  • Only SGA clones generated from tissue and body fluid samples were kept. (samples from Brain Myeloid cells and T cells will be integrated in future analyses)
🧠 Last Gift cohort HIV Reservoir CNS Clusters
cluster1 Cluster size tMRCA (median) lower HPD upper HPD Basal Ganglia Frontal Cortex (motor) Hippocampus Occipital Cortex Spinal Cord (cervical) Spinal Cord (lumbosacral) Spinal Cord (thoracic) Pons Medulla Parietal Cortex
LG03
Cluster 1 17 2005-02-01 1994-04-01 2011-05-22 4 (24%) 1 (6%) 2 (12%) 1 (6%) 3 (18%) 3 (18%) 3 (18%)
LG05
Cluster 1 17 2015-09-11 2013-05-12 2017-03-06 6 (35%) 5 (29%) 1 (6%) 1 (6%) 4 (24%)
LG14
Cluster 1 21 2014-06-10 2011-12-16 2016-05-11 7 (33%) 5 (24%) 4 (19%) 5 (24%)
LG18
Cluster 1 6 2005-12-08 1994-11-26 2014-03-02 6 (100%)
LG22
Cluster 1 10 2022-05-26 2022-01-14 2022-08-28 8 (80%) 2 (20%)
LG23
Cluster 1 6 2020-10-29 2020-06-15 2021-02-12 1 (17%) 1 (17%) 4 (67%)
LG25
Cluster 1 8 2020-11-27 2020-08-15 2021-02-09 4 (50%) 2 (25%) 2 (25%)
LG29
Cluster 1 6 2021-01-27 2019-07-31 2021-07-10 6 (100%)
LG30
Cluster 1 20 2020-03-27 2018-02-20 2021-07-13 8 (40%) 1 (5%) 1 (5%) 8 (40%) 2 (10%)
Cluster 2 7 2018-05-23 2012-03-28 2021-03-05 5 (71%) 2 (29%)
Cluster 3 7 2021-10-31 2020-04-27 2022-09-08 7 (100%)
LG33
Cluster 1 38 2008-09-26 2003-03-23 2012-11-15 5 (13%) 8 (21%) 1 (3%) 7 (18%) 5 (13%) 3 (8%) 9 (24%)
Cluster 2 15 2008-06-27 2002-06-12 2012-10-19 4 (27%) 8 (53%) 3 (20%)
LG42
Cluster 1 22 2023-05-14 2023-02-03 2023-07-29 3 (14%) 5 (23%) 6 (27%) 7 (32%) 1 (5%)
LG44
Cluster 1 12 2021-06-27 2019-11-11 2023-03-15 1 (8%) 4 (33%) 3 (25%) 4 (33%)
Cluster 2 17 2020-01-31 2017-11-24 2021-06-29 6 (35%) 5 (29%) 2 (12%) 2 (12%) 2 (12%)
LG48
Cluster 1 6 2024-02-18 2023-02-24 2024-09-29 1 (17%) 2 (33%) 1 (17%) 1 (17%) 1 (17%)
Cluster 2 7 2023-07-27 2021-07-20 2024-08-02 3 (43%) 4 (57%)
LG49
Cluster 1 8 2021-07-09 2017-06-11 2023-08-19 4 (50%) 2 (25%) 2 (25%)
1 Using most recently single genome sequencing env data

Phylogenies

Interpretation/Discussion

  • Early vs late CNS seeding
  • Evidence for CNS-restricted evolution
  • Within-host compartmentalization patterns

  • Inclusion Criteria:

    • virally suppressed
    • SGA available in both CNS and periphery
    • CSF data

it would end up with a population size of 16 :

final_population
 [1] "LG03" "LG05" "LG12" "LG14" "LG15" "LG18" "LG22" "LG24" "LG28" "LG29"
[11] "LG33" "LG34" "LG37" "LG39" "LG43" "LG49"

Thank you